Historically, GPU-based HPC applications have had a substantial memory bandwidth advantage over CPU-based workloads due to using GDDR rather than DDR memory. However, past GPUs required a restricted programming model where application data was allocated up front and explicitly copied into GPU memory before launching a GPU kernel by the programmer. Recently, GPUs have eased this requirement and now can employ on-demand software page migration between CPU and GPU memory to obviate explicit copying. In the near future, CCNUMA GPU-CPU systems will appear where software page migration is an optional choice and hardware cache-coherence can also support the GPU accessing CPU memory directly. In this work, we describe the trade-offs and considerations in relying on hardware cache-coherence mechanisms versus using software page migration to optimize the performance of memory-intensive GPU workloads. We show that page migration decisions based on page access frequency alone are a poor solution and that a broader solution using virtual address-based program locality to enable aggressive memory prefetching combined with bandwidth balancing is required to maximize performance. We present a software runtime system requiring minimal hardware support that, on average, outperforms CC-NUMA-based accesses by 1.95x, performs 6% better than the legacy CPU to GPU memcpy regime by intelligently using both CPU and GPU memory bandwidth, and comes within 28% of oracular page placement, all while maintaining the relaxed memory semantics of modern GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"
Historically, GPU-based HPC applications have had a substantial memory bandwidth advantage over CPU-based workloads due to using GDDR rather than DDR memory. However, past GPUs required a restricted programming model where application data was allocated up front and explicitly copied into GPU memory before launching a GPU kernel by the programmer. Recently, GPUs have eased […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,1398,20,379,546],"class_list":["post-13436","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-gpgpu-sim","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-prefetch"],"views":3173,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13436","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=13436"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13436\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13436"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13436"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13436"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}